Cervical‐YOSA: Utilizing prompt engineering and pre‐trained large‐scale models for automated segmentation of multi‐sequence MRI images in cervical cancer

Abstract

Abstract Cervical cancer is a major health concern, particularly in developing countries with limited medical resources. This study introduces two models aimed at improving cervical tumor segmentation: a semi‐automatic model that fine‐tunes the Segment Anything Model (SAM) and a fully automated model designed for efficiency. Evaluations were conducted using a dataset of 8586 magnetic resonance imaging (MRI) slices, where the semi‐automatic model achieved a Dice Similarity Coefficient (DSC) of 0.9097, demonstrating high accuracy. The fully automated model also performed robustly with a DSC of 0.8526, outperforming existing methods. These models offer significant potential to enhance cervical cancer diagnosis and treatment, especially in resource‐limited settings.

Keywords

• biomedical MRI
• image segmentation
• transforms